A multiple-input multiple-output (MIMO) communication system typically uses technologies for providing high channel performance through wireless fading channels. It is desired that channel performance be improved for MIMO communication systems. This includes providing optimal-quality channels to users and increasing data throughput using multi-user diversity.
A closed loop MIMO system typically transmits channel state information from a receiver to a transmitter. Thus, precoding/beamforming can provide channel optimization by utilizing the channel state information feedback from the receiver. However, transmitting the channel state information typically uses bandwidth that could otherwise be available for transmitting data traffic.
With regard to precoding, current precoding methods for multiuser (MU)-MIMO systems are typically based on a block diagonalization algorithm which utilizes precoding to force an efficient channel to be in the form of a block diagonal matrix and then to typically cancel inter-user interference only. However, when using these methods, the orthogonality between spatial beams is typically not guaranteed and therefore, such channel optimization methods are typically not efficient. In addition, there is also typically a constraint on the dimension of the antenna configuration in such MIMO systems, the constraint being the number of transmitting antennas should not be smaller than the total number of receiving antennas. One problem that may arise when using these methods is that this constraint is typically not satisfied in practical cases.
One alternative precoding method is described in US 2006/0120478. As described therein, the method performs QR decomposition on channel information being fed back from a receiver using singular value decomposition (SVD) and performs beamforming based on the QR decomposition results. However, using this method, the interferences of other users typically can not be cancelled by the beamforming. Since there is typically no other users' channel information in each receiver, it is practically difficult to eliminate inter-user interference at each receiver. Thus, high data throughput cannot be practically achieved using this precoding method.
In the above precoding methods, the channel state information of each receiver in MU-MIMO systems is typically directly fed back from the receivers to the transmitter for calculating a precoding matrix. Thus, one problem that may arise is that the increased feedback overhead typically results in bandwidth non-efficiency.
With regard to reducing feedback overhead, there are a number of methods that utilize codebooks known to both the transmitter and receiver. The codebooks typically contain precoding information that the transmitter may use for precoding. The receiver identifies the codebook elements for the transmitter to use by transmitting indices identifying the codebook elements. However, these codebook-based precoding methods are typically only used for single user (SU)-MIMO systems since there is no other users' channel information available in each receiver in MU-MIMO systems. In other words, in MU-MIMO systems, the receivers typically cannot identify the codebook elements to be used for precoding in the transmitter directly.
Hence, there exists a need for a method and system for communication channel optimization in a multiple-input multiple-output (MIMO) communication system to address at least one of the above problems.